No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
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jev-align is an experimental CLI from Sutro for building AI Functions with TypeSafe's Jev. It finds uncertain examples, asks you to label them, and uses GEPA to improve the function. Use it in your application and keep learning from production examples. Demo https://github.com/user-attachments/assets/81650587-e3f1-4655-8213-ed5f6e120e9a Quick start Requires Python 3.11 or newer. Start the CLI with either or . Use if you do not use uv. The guided setup discovers local CSV, Parquet, and JSONL files and includes three ready-to-run examples. How it works Each round: Evaluates the configured dataset and measures uncertainty. Selects ambiguous rows plus a random audit sample for you to label. Uses your accumulated labels and optional rationales to run GEPA. Shows the score, certainty change, and proposed definition diff. Lets you accept, reject, rewind, or resume later. Every label comes from you. A higher training score never accepts a proposal automatically. Task types | Multilabel
| Stars | 205 |
| Forks | 16 |
| Language | Python |
| Category | AI Tool |
| License | Apache-2.0 |
| Quality Score | 72.3602702982169/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-19 |
| Created | 2026-09-19 |
| Platforms | cli, python |
| Est. Tokens | ~14k |
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jev-align is Build calibrated AI classifiers from human feedback using Jev and GEPA.. It is categorized as a AI Tool with 205 GitHub stars.
jev-align is primarily written in Python. It covers topics such as active-learning, classification, cli.
You can find installation instructions and usage details in the jev-align GitHub repository at github.com/sutro-sh/jev-align. The project has 205 stars and 16 forks, indicating an active community.
jev-align is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to jev-align on Agent Skills Hub include awesome-jev-by-typesafe, skillranker, phi. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: